This document discusses map integration methods for road network data from two sources, the Virginia Department of Transportation's Linear Referencing System (LRS) and proprietary data from INRIX (XD). Two methods, spatial join and transfer attributes, are evaluated on five Virginia interstates. Spatial join joins features based on intersecting geometry, while transfer attributes joins on common attributes within a search distance. The accuracy of each method is calculated based on the number of features that match between the datasets. Spatial join is tested using different coordinate systems and LRS layers, while transfer attributes varies the search distance. Visualizing the buffers helps understand how distance affects matching.
What to do with the existing spatial data in planningKarel Charvat
ย
Spatial planning acts between all levels of government so planners face important challenges in the development of territorial frameworks and concepts every day.
Spatial planning systems, the legal situation and spatial planning data management are completely different and fragmented throughout Europe.
Nevertheless, planning is a holistic activity.
All tasks and processes must be solved comprehensively with
input from various sources.
It is necessary to make inputs interoperable because it allows the user to search data from different sources, view them, download them and use them with help of geoinformation technologies (GIT).
IJRET-V1I1P3 - Remotely Sensed Images in using Automatic Road Map CompilationISAR Publications
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High Resolution satellite Imagery is an important source for road network extraction for
roads database creation, refinement and updating. Various sources of imagery are known for their
differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for
different purposes of vegetation mapping. A number of shape descriptors are computed to reduce
the misclassification between road and other spectrally similar objects. The detected road segments
are further refined using morphological operations to form final road network, which is then
evaluated for its completeness, correctness and quality. The proposed methodology has been tested
on updating on road extraction from remotely-sensed imagery.
Introduction of GIS, components of gis, Data input and data out
spatial data, attribute data, spatial data collection joining spatial and attribute data in gis operations
What to do with the existing spatial data in planningKarel Charvat
ย
Spatial planning acts between all levels of government so planners face important challenges in the development of territorial frameworks and concepts every day.
Spatial planning systems, the legal situation and spatial planning data management are completely different and fragmented throughout Europe.
Nevertheless, planning is a holistic activity.
All tasks and processes must be solved comprehensively with
input from various sources.
It is necessary to make inputs interoperable because it allows the user to search data from different sources, view them, download them and use them with help of geoinformation technologies (GIT).
IJRET-V1I1P3 - Remotely Sensed Images in using Automatic Road Map CompilationISAR Publications
ย
High Resolution satellite Imagery is an important source for road network extraction for
roads database creation, refinement and updating. Various sources of imagery are known for their
differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for
different purposes of vegetation mapping. A number of shape descriptors are computed to reduce
the misclassification between road and other spectrally similar objects. The detected road segments
are further refined using morphological operations to form final road network, which is then
evaluated for its completeness, correctness and quality. The proposed methodology has been tested
on updating on road extraction from remotely-sensed imagery.
Introduction of GIS, components of gis, Data input and data out
spatial data, attribute data, spatial data collection joining spatial and attribute data in gis operations
ESWC2015 - Tutorial on Publishing and Interlinking Linked Geospatial DataKostis Kyzirakos
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In this tutorial we present the life cycle of linked geospatial data and we focus on two important steps: the publication of geospatial data as RDF graphs and interlinking them with each other. Given the proliferation of geospatial information on the Web many kinds of geospatial data are now becoming available as linked datasets (e.g., Google and Bing maps, user-generated geospatial content, public sector information published as open data etc.). The topic of the tutorial is related to all core research areas of the Semantic Web (e.g., semantic information extraction, transformation of data into RDF graphs, interlinking linked data etc.) since there is often a need to re-consider existing core techniques when we deal with geospatial information. Thus, it is timely to train Semantic Web researchers, especially the ones that are in the early stages of their careers, on the state of the art of this area and invite them to contribute to it.
In this tutorial we give a comprehensive background on data models, query languages, implemented systems for linked geospatial data, and we discuss recent approaches on publishing and interlinking geospatial data. The tutorial is complemented with a hands-on session that will familiarize the audience with the state-of-the-art tools in publishing and interlinking geospatial information.
http://event.cwi.nl/eswc2015-geo/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
I compared various ways of transportation modeling. The traditional, four-step model was demonstrated using FSUTMS (the standard Florida model, running on CUBE/Voyager). The activity-based model for South Florida was in development at the time, but not yet ready for prime time. The paper analyzed the benefits of the newer activity-based methodology, which is essentially a form of agent-based modeling. Since popular city simulation games such as SimCity 5 use agent-based modeling, I demonstrated how this works with a similar program (Cities in Motion 2) and suggested that this type of game could be used in planning, perhaps even as a public involvement tool to let citizens see firsthand how a scenario might play out.
Analysis of Webspaces of the Siberian Branch of the Russian Academy of Scienc...ITIIIndustries
ย
In this paper, two webspaces of academic institutions of the Siberian Branch of Russian Academy of Sciences (SB RAS) and of the Fraunhofer-Gesellschaft (FG), Germany, will be investigated. The webspaces are represented by directed graphs possessing vertices corresponding to websites. An arc connects two vertices if there exists at least one hyperlink between the corresponding websites. Webometrics is used for ranking the websites of SB RAS and FG. We discuss numerical results when studying the websites structurally. In particular, we examine scientific communities of the underlying websites representing directed graphs and draw important conclusions.
2011 ITS World Congress - GO-Sync - A Framework to Synchronize Transit Agency...Sean Barbeau
ย
Discusses an open-source tool that can sync GTFS datasets with OpenStreetMap to help small agencies manage their bus stop inventory via crowd-sourcing. Includes some actual results of crowd-sourcing bus stop location accuracy in Tampa, FL.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Infrastructure Development of Aarey colony Mumbai on GIS platform - A case studyAM Publications
ย
Roads are considered as the measure of the Infrastructure development of the country. Roadway construction planning
involves dealing with a number of activities and information simultaneously. A large amount of information regarding design,
construction methodology to be followed, quantities, unit costs & production rates, etc is to be continuously processed & refined.
Doing this manually is time consuming & cumbersome as the size of the projects today has increased manifold. Geographic
Information System (GIS) is a very effective tool for integrating & managing various types of information required for roadway
construction planning. This piece of work tries to understand how GIS can be applied for improving roadway construction planning.
It also aims at developing a system which can help construction planners to make a proper decision with features like space scheduling & activity sequence visualization process.
Big data traffic management in vehicular ad-hoc network IJECEIAES
ย
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
Cut to Fit: Tailoring the Partitioning to the Computationjackkolokasis
ย
Iacovos G. Kolokasis & Polyvios Pratikakis
Institute of Computer Sciense (ICS)
Foundation of Research and Technology โ Hellas (FORTH) &
Computer Science Department, University of Crete
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
Modeling a geo spatial database for managing travelers demandijdms
ย
The geo-spatial database is a new technology in database systems which allow storing, retrieving and
maintaining the spatial data. In this paper, we seek to design and implement a geo-spatial database for
managing the travelerโs demand with the aid of open-source tools and object-relational database package.
The building of geo-spatial database starts with the design of data model in terms of conceptual, logical
and physical data model and then the design has been implemented into an object-relational database. The
geo-spatial database is developed to facilitate the storage of geographic information (where things are)
with descriptive information (what things are like) into the vector model. The developed vector geo-spatial
data can be accessed and rendered in the form of map to create the awareness of existence of various
services and facilities for prospective travelers and visitors.
ESWC2015 - Tutorial on Publishing and Interlinking Linked Geospatial DataKostis Kyzirakos
ย
In this tutorial we present the life cycle of linked geospatial data and we focus on two important steps: the publication of geospatial data as RDF graphs and interlinking them with each other. Given the proliferation of geospatial information on the Web many kinds of geospatial data are now becoming available as linked datasets (e.g., Google and Bing maps, user-generated geospatial content, public sector information published as open data etc.). The topic of the tutorial is related to all core research areas of the Semantic Web (e.g., semantic information extraction, transformation of data into RDF graphs, interlinking linked data etc.) since there is often a need to re-consider existing core techniques when we deal with geospatial information. Thus, it is timely to train Semantic Web researchers, especially the ones that are in the early stages of their careers, on the state of the art of this area and invite them to contribute to it.
In this tutorial we give a comprehensive background on data models, query languages, implemented systems for linked geospatial data, and we discuss recent approaches on publishing and interlinking geospatial data. The tutorial is complemented with a hands-on session that will familiarize the audience with the state-of-the-art tools in publishing and interlinking geospatial information.
http://event.cwi.nl/eswc2015-geo/
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
I compared various ways of transportation modeling. The traditional, four-step model was demonstrated using FSUTMS (the standard Florida model, running on CUBE/Voyager). The activity-based model for South Florida was in development at the time, but not yet ready for prime time. The paper analyzed the benefits of the newer activity-based methodology, which is essentially a form of agent-based modeling. Since popular city simulation games such as SimCity 5 use agent-based modeling, I demonstrated how this works with a similar program (Cities in Motion 2) and suggested that this type of game could be used in planning, perhaps even as a public involvement tool to let citizens see firsthand how a scenario might play out.
Analysis of Webspaces of the Siberian Branch of the Russian Academy of Scienc...ITIIIndustries
ย
In this paper, two webspaces of academic institutions of the Siberian Branch of Russian Academy of Sciences (SB RAS) and of the Fraunhofer-Gesellschaft (FG), Germany, will be investigated. The webspaces are represented by directed graphs possessing vertices corresponding to websites. An arc connects two vertices if there exists at least one hyperlink between the corresponding websites. Webometrics is used for ranking the websites of SB RAS and FG. We discuss numerical results when studying the websites structurally. In particular, we examine scientific communities of the underlying websites representing directed graphs and draw important conclusions.
2011 ITS World Congress - GO-Sync - A Framework to Synchronize Transit Agency...Sean Barbeau
ย
Discusses an open-source tool that can sync GTFS datasets with OpenStreetMap to help small agencies manage their bus stop inventory via crowd-sourcing. Includes some actual results of crowd-sourcing bus stop location accuracy in Tampa, FL.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Infrastructure Development of Aarey colony Mumbai on GIS platform - A case studyAM Publications
ย
Roads are considered as the measure of the Infrastructure development of the country. Roadway construction planning
involves dealing with a number of activities and information simultaneously. A large amount of information regarding design,
construction methodology to be followed, quantities, unit costs & production rates, etc is to be continuously processed & refined.
Doing this manually is time consuming & cumbersome as the size of the projects today has increased manifold. Geographic
Information System (GIS) is a very effective tool for integrating & managing various types of information required for roadway
construction planning. This piece of work tries to understand how GIS can be applied for improving roadway construction planning.
It also aims at developing a system which can help construction planners to make a proper decision with features like space scheduling & activity sequence visualization process.
Big data traffic management in vehicular ad-hoc network IJECEIAES
ย
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
Cut to Fit: Tailoring the Partitioning to the Computationjackkolokasis
ย
Iacovos G. Kolokasis & Polyvios Pratikakis
Institute of Computer Sciense (ICS)
Foundation of Research and Technology โ Hellas (FORTH) &
Computer Science Department, University of Crete
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
Modeling a geo spatial database for managing travelers demandijdms
ย
The geo-spatial database is a new technology in database systems which allow storing, retrieving and
maintaining the spatial data. In this paper, we seek to design and implement a geo-spatial database for
managing the travelerโs demand with the aid of open-source tools and object-relational database package.
The building of geo-spatial database starts with the design of data model in terms of conceptual, logical
and physical data model and then the design has been implemented into an object-relational database. The
geo-spatial database is developed to facilitate the storage of geographic information (where things are)
with descriptive information (what things are like) into the vector model. The developed vector geo-spatial
data can be accessed and rendered in the form of map to create the awareness of existence of various
services and facilities for prospective travelers and visitors.
This Seminar presentation is made by Shrikrishna Kesharwani
1ST YEAR, Transportation engineering student
NIT WARANGAL
FOLLOW ME ON INSTAGRAM
@SHRIKRISHNAKESHARWANI
With the rapid development in Geographic Information Systems (GISs) and their applications, more and
more geo-graphical databases have been developed by different vendors. However, data integration and
accessing is still a big problem for the development of GIS applications as no interoperability exists among
different spatial databases. In this paper we propose a unified approach for spatial data query. The paper
describes a framework for integrating information from repositories containing different vector data sets
formats and repositories containing raster datasets. The presented approach converts different vector data
formats into a single unified format (File Geo-Database โGDBโ). In addition, we employ โmetadataโ to
support a wide range of usersโ queries to retrieve relevant geographic information from heterogeneous and
distributed repositories. Such an employment enhances both query processing and performance.
This is most benificial for the First year Engineering students.This presentation consists of videos and many applications of GIS. The processes and the other parts of GIS is also nicely explained.
A low cost method of real time pavement condition data sharing to expedite ma...UVision
ย
A low cost method of real time pavement condition data sharing to expedite maintenance intervention
Pavements for roads in cities and highways are degraded with potholes, cracking, and rutting distresses. There is a strong need to identify these locations and sections with undesired longitudinal roughness quickly and accurately every year. Traditionally, expensive standalone survey vehicles for roughness measurements and more expensive multi-function vehicles are employed by highway agencies or through contract services, which most cities and local agencies canโt afford. The primary objective of this study is to describe a low cost method to collect essential pavement condition data and share real time to expedite maintenance intervention needs. This facilitates rapid identification of pavement sections with undesired longitudinal roughness and local defects. This paper discusses the impact of social media, crowd sourcing, and advances in cheaper accurate motion sensors and cloud server data processing. These tools make it possible to develop easy-to-use low cost methods, which are affordable by city public work and smaller road agencies.
FORMATION OF SPATIAL DATABASES WITHIN THE SPATIAL DATA INFRASTRUCTUREIAEME Publication
ย
The article is devoted to the question of designing, creating and updating spatial
databases within the framework of spatial data infrastructures (SDI). The paper deals
with key aspects of the development of models of spatial-temporal data based on the
study of landscapes, as well as provides the main directions for updating the
geospatial repository of information based on remote sensing data of the Earth.
Applying association rules and co location techniques on geospatial web services
ย
Understanding Map Integration Using GIS Software_ff
1. UNDERSTANDING MAP INTEGRATIONUSING GIS SOFTWARE
Submitted July 29, 2016
Michelle Pasco
Undergraduate Research Assistant
Civil and Environmental Engineering Department
Old Dominion University
135 Kaufman Hall, Norfolk, VA 23529
E-mail: pasco.michelle@gmail.com
Total Word Count = 3880 words + 7 Figures * 250 + 4 Tables * 250 = 6630 words
2. Pasco 2
I. ABSTRACT
Geospatial data stored in Geographical Information Systems (GIS) is beneficial to researchers
because it can relay ground-truth information and numerous datasets can simultaneously be
observed. With the rise of technological advancements, map integration has been introduced
as an innovative technique for studying geographic-enabled data as it can provide a differing
viewpoints and insights on scientific research. Although map integration can be advantageous
in analyzing data, accurate results are difficult to obtain due to spatial displacement and
attribute disparity. These issues include geometric discrepancies, different data structures, and
altered representation. This project focuses on understanding the issues that follow the map
integration process and discovering methods that can increase the accuracy of the process. In
the context of map integration for road networks, two methods, spatial join and transfer
attributes, were studied that were shown to offer favorable and encouraging results. The
research also was able to discern between the cases when one method is more appropriate
than the other.
II. INTRODUCTION
Background: Conflation is an important part of geo-scientific research. It is the integration of
two or more spatial datasets, most of the time represented as maps, and it is used to acquire a
better understanding of the data that cannot be done by researching them individually. There
are two types of map conflation: horizontal and vertical. Horizontal conflation is edge-matching
adjacent maps while removing any differences in spatial content. Vertical conflation is the
combination of two or more maps of the same region that have data structure and thematic
disparity (1). For this project, vertical conflation will be used as the project covers two road
networks of same area, namely the Commonwealth of Virginia. To gather more information
about the geographical data sets, conflating them is often done within GIS and can be made
available for private or public creation and distribution (2). Although map integration is
beneficial, it is susceptible to a number of issues surrounding the process.
Problem: Traffic data is valuable for federal, state, and local governments, businesses, and
engineering. Using traffic geospatial data, several pieces of information (fields) or status reports
can be collected about incidents, weather events and other types of event and can be stored
electronically into one file or an archive (3). Often times there are industries that publish their
individual data sets specific to their demand or research. Users have the ability to integrate
multiple geospatial data using GIS. Although this is beneficial to the user, map conflation faces
difficulties that are not easily resolved. Currently, research is being done on how to correct the
issues by creating semi-automatic or automatic codes, iterations, and equations (4). The issues
that arise during the conflation process are geometrical discrepancies such as differences in
3. Pasco 3
length, position, direction, size, shape of features; or overlaps, gaps, and duplication in features
(5). There can also be prior process differences such as data structure or in feature
representation.
Faulty data integration begins with one of the following: unequal updating periods, equal data
models acquired by different operators, unequal data models, and content differences (1). The
updating period is critical because in order to maintain accurate map information there may be
continual transformation of the maps in a certain time frame. Two examples would be roadway
reconstruction or natural occurrences such as erosion on shores. Datasets can also have
different operators, which means that the data can be interpreted and represented differently.
Due to this fact, different Geographic Coordinate Systems (GCS) and the present of unique data,
like frontage roads, in only one of the maps can affect the conflation process and ultimately the
accuracy. This also links to conflicting (unequal) data model structures, primarily referring to
the segmentation system. Finally, it is highly unlikely to have datasets that all describe the same
data sets. In previous work, conflation has been done between a geo-scientific dataset and a
topographic dataset resulting in large geometric discrepancies (1). For this project, two road
networks are being conflated that were generated by different industries, and have different
and feature representation, road geometry, and attribute aspects.
Objective: The objective is to improve the conflation process and mitigate the resulting
problems. Unfortunately, spatial displacement aspect is often non-systematic, so it will be
difficult to pinpoint the reasons as to why it conflates inaccurately (2). This project utilizes
previous data and attempts to conflate by using several methods within GIS to further
understand the issues following the integration process.
III. STUDY AREA
Data Sets: The two data sets that were used in conflation in this work were:
๏ท VDOT (Virginia Department of Transportation) LRS (Linear Referencing System) road
network, published on August 2015 and available at ArcGIS Online URL here
๏ท INRIX XD proprietary road network for Virginia dated December 2014 that was
purchased by VDOT and made available to the researchers.
In addition to these data sets, Google Earth from January 2016 and Google Maps from 2016
were used to aid in the conflation process when the discrepancies between the two data sets to
be conflated could not be immediately resolved.
Study Area: The Virginia road network was studied with specific focus on five interstates: I-64,
I-564, I-95, I-395, and I-495. These interstates pass through three urban regions including
4. Pasco 4
Hampton Roads (HR), Richmond, and Northern Virginia (NOVA). The rural areas are along the
two main interstates, I-64 and I-95 outside of the three urban areas.
Figure 1: Study Area Map
I-64 runs from the east coast of Virginia into West Virginia. It passes through Hampton Roads,
which includes cities such as Hampton, Norfolk, Virginia Beach, and Chesapeake. I-564 is a
branch from I-64 which connects the Norfolk Naval Base to downtown Norfolk. I-64 also runs
through Virginiaโs capital, Richmond, which is where I-95 and I-64 intersect. In Virginia I-95 runs
from NOVA to the North Carolina border. NOVA includes cities such as Alexandria, Arlington,
and Manassas and is within close proximity to the nationโs capital, Washington D.C. Interstate
I-395 runs from I-95/I-495/I-395 Springfield-Franconia Interchange to the Washington DC. In
this project, I-495 denotes only the Virginia section of the Capital Beltway. As mentioned
before, for the purpose of this project, all areas along I-64 and I-95 that are outside of the three
urban cities will be considered rural.
IV. METHODOLOGY
Data: The first step was to understand the issues surrounding map migration and applying that
understanding to the two data sets. We studied two different data sources: Virginia
Department of Transportationโs (VDOT) Linear Referencing System (which from now on will be
called โLRSโ in this paper) and INRIX XD (which will be called from now on โXDโ in this paper).
The VDOT LRS map data is public and is it used by the private sector and the general public to
study VDOT maintained roadways and compile business data (5). The map document is updated
quarterly to ensure accurate road networks by eliminating previous geometric discrepancies
and adjusting old roads.
Within the LRS, the road segments are defined by a start and end point. The LRS also contains
multiple layers, within GIS, that contain different attributes for unique purposes. This project
used the specific layer called โSDE_VDOT_EDGE_RTE_OL_MPST_LRSโ. Here the layer name
5. Pasco 5
stands for โSpatial Database Engine (SDE) VDOT EDGE (segmentation system) Route (RTE)
Overlap (OL) Milepost (MPST) LRS.โ The segments are based off of mileposts displayed in
Virginia. The LRS contains all the VDOT-maintained roads in Virginia. The features in this layer of
the LRS are road links, usually directional and short in length, that are broken at significant
points in the geometry of the road.
INRIX is a company that provides traffic data and services through connected devices that
include road speed parameters, incident information, and text alerts. The speed and travel time
information is based mainly on vehicle probes (1). This information is delivered based on
geographical links called XD segments, which define specific sections of roadways (6). XDโs road
system is not as specific as LRS and only provides certain roads that are within LRSโs secondary
and urban roads for which INRIX can collect probe data. Like the LRS links, the XD links are
directional and short in length and are broken at logical points. Generally, the XD links are
shorter than 1.5 miles, but many of them are longer than LRS links.
VDOT also provided a sample conflation between the LRS and XD road networks. When
observing this sample conflation, it was apparent that it had gaps and overlaps. After further
research and cross referencing with Google Earth and Google Maps, it was concluded that
possible reasons for these issues might have included disjointed segmentation in the road
geometry, road geometric disparity, mismatched attributes, and outdated information. To
further understand these issues, this project attempted to perform a conflation of the five
study area roadways using several methods in GIS:
๏ท Spatial Join (with variants of same and different GCS and EDGE- vs. non-EDGE matching),
๏ท Transfer Attributes,
๏ท Other methods (edge-matching and join by attributes).
All the analysis for this work was done using the ESRI ArcGIS software suite.
Spatial Join: Spatial join is joining features by intersecting their geometry (7). Dataset objects
are digitized with vector components such as points, lines, and polygons. With this tool, the
vector components for each of the two datasets become either the โsourceโ or โtargetโ layer.
After the spatial join conflation process, an output field called โCount_โ is created on the
attribute table. This โCount_โ is the number of intersections (or matches) that each feature has.
6. Pasco 6
Figure 2: The "Count_" field is shown in light blue. 0 indicates that the feature did not match with any other features.
In this project, the LRS was set as the source layer and the XD was set as the target layer. In an
attempt to increase the accuracy, the influence of two factors was studied. The first factor was
the geographical coordinate system, with the possibilities of keeping the original GCS and
converting to a common GCS. In the first case, the spatial join was performed with the LRS and
XD in their respective original GCSs. LRS had โGCS_North_American_1983โ GCS and XD had
โGCS_WGS_1984โ GCS as their original GCS. In the second attempt the same GCS was used for
both datasets. XDโs GCS was projected to match the LRSโs GCS using the Project tool under Data
Management Toolbox in GIS.
The second factor was the layer within the LRS: EDGE and Non-EDGE. EDGE means that the
roads are broken up into multiple segments to increase accuracy of the road geometry. โNon-
EDGEโ means that the road is made of a two segments with each segment bearing the direction
of the road. For example, I-64โs two segments are labeled as I-64 west and I-64 east.
Taking into account these two situations, each with two options, four sets of spatial join
conflations were generated in total.
7. Pasco 7
Figure 3: GCS vs Original (ORG) spatial join case on part of I-64 where the top segments are the source and target layers and
the bottom segments are the conflations
To increase the GIS processing speed, each of the spatial joins was done individually for each of
the five interstates. After the completion of each spatial join, the attribute table was analyzed
to see how many features had matches in the โCount_โ field. An equation was created to
measure the โConflation Accuracyโ to study the relationship between the count and spatial join
accuracy:
(๐๐๐ข๐๐ก > 0) ๐๐๐๐ก๐ข๐๐๐
๐ก๐๐ก๐๐ # ๐๐ ๐๐๐๐ก๐ข๐๐๐
โ 100 = ๐๐๐๐๐๐๐ก๐๐๐ ๐๐๐๐ข๐๐๐๐ฆ, ๐๐ (%)
where count = 0 means that the feature did not match and count > 0 represents the features
that did match. Once each of the study areas for the two comparisons was completed, the
average was taken to discover which of the two was more accurate.
Transfer Attributes: Transfer attributes is joining attributes in one dataset to corresponding
attributes in another dataset (7). Similar to spatial join, there must be a source and target layer.
An additional requirement is that each of the layers needs to have the same GCS. Then, one or
more common attributes between the two datasets are selected along with a search distance
to match features. After the conflation process, the selected field(s) is (are) transferred into the
attribute table of the target layer where it will show the values that have matched with the
source layer values. If <Null> values appear, then it means that the target features failed to
match with the source features.
8. Pasco 8
Figure 4: The left column is the transferred field from the source layer (LRS) to the target layer (XD).
The LRS was used as the source layer and the XD was used as the target layer. The common
field that was used for matching is called Route Common Name or โRTE_COMMON_NM.โ Four
search distances were used were 0.1 mi, 0.3 mi, 0.5 mi, and 1 mi in an attempt to find the one
that would contribute to the most accurate results. These distances were chosen because they
were will within estimated bounds for accurate conflation, yet broad enough to provide a
reasonable sample. For research purposes, 0.05 mi, 2 mi, and 5 mi were also tested but they
yielded poor conflation results and so they were discarded. After recording the data, a similar
equation to spatial join was used to calculate the conflation accuracy:
๐๐ < ๐๐ข๐๐ > ๐๐๐๐ก๐ข๐๐๐
๐ก๐๐ก๐๐ # ๐๐ ๐๐๐๐ก๐ข๐๐๐
โ 100 = ๐๐๐๐๐๐๐ก๐๐๐ ๐๐๐๐ข๐๐๐๐ฆ, ๐๐ (%)
where โno <Null>โ means that the features that did not match were removed to only include
the features that did match.
9. Pasco 9
Figure 5: Comparison between 0.1 mi and 0.3 mi conflations for transfer attributes on part of I-64 where the top segments
are the source and target layers and the bottom segments are the conflations.
To further understand how the search distance affects the conflation result, the buffer tool was
used to visually observe the relationship between the search distance and roads. The tool
creates a buffer surrounding a feature that is determined by the search distance (7). First, the
source layer was LRS and the target feature was XD. Additionally, the analysis was completed in
the reverse order. Buffer has two types: round and flat. Round buffer surrounds the segment
with a radial buffer, while a flat buffer creates one that terminates at the endpoint.
Figure 6: The top segment represents each of the round buffers and the bottom segment represents the flat buffer.
Other methods: The two other methods that were tested are called โedge-matchingโ and โjoin
by attributes.โ Edge-match is a tool that physically transforms features during the conflation
process (7). Prior to using the edge-match tool, a separate tool called โgenerate edge-match
linksโ is required to assess each of the segment features. These tools need a source layer,
target layer, and require that the source and target layers have identical GCS. After using the
generate edge-match tool, an output table is created including start and end points of each
segments and an โedge-match confidenceโ or โem_conf.โ Edge-match confidence scores each
10. Pasco 10
of the features on a value range of 0 to 100, 100 being the maximum confidence level. The
lower the confidence level, the less likely the segment will conflate. The project does not
include the edge-match tool because a great deal of manual intervention is needed to
transform the segments with low confidence levels to increase the accuracy. This is not suitable
for large-scale projects.
Join by attributes is similar to transfer attributes, but it forces users to choose two fields, one in
each of the datasets, in order to conflate (7). To conflate, the fields are not required to have the
same GCS, but the tool still needs a source and target layer. The process joins the source layerโs
attribute table to the target layerโs attribute table, attempting to match corresponding
features. If there is no correlation between the layers, the target datasetโs attribute table will
be recorded as a <Null> value. The project did not utilize the join by attributes because it is
difficult to acquire two fields that will match the features within two datasets and involves
several rounds of trial and error. The common result is that all of the target attribute table will
be <Null>.
V. RESULTS
Spatial Join: The table below displays the result of 20 spatial joins for the four cases.
Table 1: Spatial join results.
Road Name &
Spatial Join Type
# of
features
Count>0
features
Conflation
Accuracy,
ca (%)
Average ca
64_EDGE_ORG 728 632 86.81
75.11
564_EDGE_ORG 12 10 83.33
95_EDGE_ORG 573 452 78.88
395_EDGE_ORG 136 89 65.44
495_EDGE_ORG 167 102 61.08
64_EDGE_GCS 728 359 49.31
44.11
564_EDGE_GCS 12 10 83.33
95_EDGE_GCS 573 287 50.09
395_EDGE_GCS 136 27 19.85
495_EDGE_GCS 167 30 17.96
64_NON_ORG 2 2 100.00
100.00
564_NON_ORG 2 2 100.00
95_NON_ORG 2 2 100.00
395_NON_ORG 2 2 100.00
495_NON_ORG 2 2 100.00
64_NON_GCS 2 2 100.00 100.00
11. Pasco 11
564_NON_GCS 2 2 100.00
95_NON_ GCS 2 2 100.00
395_NON_ GCS 2 2 100.00
495_NON_ GCS 2 2 100.00
Where:
๏ท The number represents the interstate
๏ท โEDGEโ represents the EDGE layer in LRS
๏ท โORGโ represents the original XD
๏ท โGCSโ represents the Geographic Coordinate System or the projected XD layer
๏ท โNONโ represents the Non-EDGE layer in LRS.
In the GCS versus ORG cases, the recorded data shows that the ORG is 30% more accurate in
the EDGE layer and there is no difference in the Non-EDGE layer. In the EDGE versus Non-EDGE
cases, Non-EDGE is 100% accurate due to the fact that there are only two segments for each
interstate. Although Non-EDGE seems to be complete, the XD matched with the entire road
instead of a segment on the LRS. This will not be sufficient for many applications that would use
the conflated datasets. Since both Non-EDGE comparisons resulted identically, further
investigation of the count for each feature was done.
Table 2: NON-EDGE segments count results.
Road Name &
Spatial Join Type First Segment
Match
Count Second Segment
Match
Count
64_NON_ORG I-64E 628 I-64W 620
564_NON_ORG I-564E 9 I-564W 15
95_NON_ORG I-95S 443 I-95N 438
395_NON_ORG I-395S 84 I-395N 96
495_NON_ORG I-495S 77 I-495N 79
64_NON_GCS I-64E 190 I-64W 208
564_NON_GCS I-564E 4 I-564W 2
95_NON_GCS I-95S 127 I-95N 146
395_NON_GCS I-395S 12 I-395N 23
495_NON_GCS I-495S 9 I-495N 7
In the table above N, S, E, and W are North, South, East, and West, respectively. According to
the data, the ORG layer receives more matches than the GCS layer. The greater the count
number is, the more trouble GIS will have matching the correct segments. Although it may
seem that ORG is less accurate than GCS, there is a chance that it is actually the opposite. GCS is
12. Pasco 12
less accurate because projecting a shape file to a different GCS induces geometric
discrepancies. Therefore, it was concluded that the main reason behind the NON_GCS case
having a lower count is because GIS is not reading the segments correctly.
Transfer Attributes: The table below displays the result of 20 transfer attribute conflations.
Table 3: Transfer attributes results.
Road Name &
Search
Distance
# of
features
No <Null>
features
Conflation
Accuracy,
ca (%)
I-64_0.1 mi 754 686 90.98
I-564_0.1 mi 11 8 72.73
I-95_0.1 mi 477 435 91.19
I-395_0.1 mi 64 60 93.75
I-495_0.1 mi 52 50 96.15
I-64_0.3 mi 754 689 91.38
I-564_0.3 mi 11 8 72.73
I-95_0.3 mi 477 435 91.19
I-395_0.3 mi 64 61 95.31
I-495_0.3 mi 52 50 96.15
I-64_0.5 mi 754 689 91.38
I-564_0.5 mi 11 8 72.73
I-95_0.5 mi 477 434 90.99
I-395_0.5 mi 64 61 95.31
I-495_0.5 mi 52 50 96.15
I-64_1 mi 754 689 91.38
I-564_1 mi 11 8 72.73
I-95_1 mi 477 434 90.99
I-395_1 mi 64 61 95.31
I-495_1 mi 52 50 96.15
Where:
๏ท โcaโ represents the โConflation Accuracyโ
๏ท โno <Null>โ is the removal of <Null> values
๏ท โmiโ represents miles
๏ท The red text indicates change in the number of โno <Null>โ features between one
search distance and the next.
The shorter the interstates are, the less chance of change there will be. This is the case for I-
564, I-395, and I-495. Observing the red text, there are subtle changes in the number of โno
<Null>โ features. After experimenting, 0.1 mi was set as the minimum search distance and 1 mi
13. Pasco 13
as the maximum search distance due to the fact that anything lower or higher than the
minimum and maximum produced inaccurate results.
Particularly on I-64, three segments matched only 0.3 mi or higher. To understand why this is,
the buffer tool was utilized to mimic how GIS processes the conflation.
Each of the LRS segments were numbered 1 thru 4 from left to right and the number of lines
that intersected with the buffer was recorded. The intersections will be referred to as matches.
The first set of results are matched using the round buffer and setting the source layer as LRS
and target layer as XD with the three search distances being 0.1 mi, 0.3 mi, and 0.5 mi. A single
flat buffer was applied with a search distance of 0.1 mi because it was apparent that the
matches would all be identical. The second case is using the same source and target layers, but
matching which XD segments intersected the buffers. The XD segments are labeled as their
feature number. The results are as followed:
Table 4: Buffer tool results.
LRS Segment
Search Distance
Round
Buffer
0.1 mi
Round
Buffer
0.3 mi
Round
Buffer
0.5 mi
Flat
Buffer
0.1 mi
LRS Segment1 1 1 1 1
LRS Segment2 3 3 3 2
LRS Segment3 2 2 2 2
LRS Segment4 2 2 2 1
XD Segment
Round
Buffer
0.1 mi
Round
Buffer
0.3 mi
Round
Buffer
0.5 mi
Flat
Buffer
0.1 mi
XD Segment1
(4100330)
2 2 2 2
XD Segment2
(4100331)
3 3 3 2
XD Segment3
(4100515)
3 3 3 2
In the table above the numbers represent the number of matches for search distance and the
buffer. Observing the results in Figure 7, the 0.1 mi round buffer intersects some of the XD
segments by an insignificant amount. When the buffer search distance is increased to 0.3 mi or
0.5 mi, GIS has enough data to read the features and correctly match.
VI. CONCLUSIONS &OUTLOOK
Understanding the different methods of conflation is crucial in measuring the accuracy of the
process. There are a few methods that are suitable for all types of research. Spatial joining is
14. Pasco 14
better to use if the data sets are comprised of many, potentially small, features or if the
datasets are comparatively similar in geometry and data structure. Transfer attributes is overall
more accurate because it matches features by not only their geometry, but also by how similar
their attributes are. This method covers the two most important aspects in the conflation
process.
Analyzing the five roadways from the study areas, it can be noted that the smaller interstates (I-
395, I-495, and I-564) seemto be more accurate than the two main interstates (I-64 and I-95).
While observing how the conflation process affected areas along the roads, it seems that there
are more gaps within the urban cities than the rural areas. The most likely cause for this is
because the roads are within close proximity of each other, so they are more likely to be
mismatched by GIS. There are few gaps and overlaps along the rural areas that are due to
geometric discrepancies or mislabeling of road names.
In the future, these methods can be used on a wider scale. For example, instead of doing the
five interstates, the entire Virginia road network can be studied. The side effects of working on
a larger scale will likely be that the conflation process will take much longer and that the
resulting road networks are most susceptible to problems such as gaps and overlaps. This is
because the closer the geometric features are, the higher the mismatching chance is. To
combat these issues, an understanding of the problems that are affiliated with the gaps and
overlaps is required. Manual intervention, such as manually transforming segments, may be
needed for some spots that are not correctly digitized. Once these issues are pinpointed and
resolved, a quasi-automatic or automatic process can be coded and used in GIS. This will create
a smoother conflation process and make it easier for spatial data and attributes for multiple
datasets to be researched.
VII. ACKNOWLEDGEMENTS
A special thanks to Simona Babiceanu, who advised me and kept me on the right path during
my research. I want to also thank Dr. Emily Parkany for the constant support and
encouragement that she gave to me and the other interns. Lastly, I want to give a warm thank
you to Daniela Gonzales for motivating me to apply for the Mid-Atlantic Transportation
Sustainability Center โ University Transportation Center (MATS UTC) program.
VIII. REFERENCES
1. G. v. Gรถsseln,M.Sester. Integration of GeoscientificData Sets and theGerman Digital Map
Using A Matching Approach.CommissionIV,WGIV/7.
http://www.cartesia.org/geodoc/isprs2004/comm4/papers/534.pdf.AccessedJune15,2016.
2. Davis,Curt H.,Haithcoat, TimothyL.,Keller,JamesM.,Song,Wenbo. Relaxation-Based Point
FeatureMatching forVectorMap Conflation,2011. TransactionsinGIS, 15(1), pg. 43-60.